Errors Affecting Data Analysis

Albert Einstein says, “If you can’t explain it simply, you don’t understand it well enough”. Well! Einstein is not wrong in saying this. Getting the real quintessence of any figure and amplifying it is the basic constraint for a successful dissertation and its findings. Also, any violation in identifying figures will cause not only copious numbers of errors, but also all your effort will reduce to rubble, thus let’s roll on our eyes on errors that can affect your data analysis –

Biased source – Source from where samples are collected can be biased and can reflect unfair data’s. As, representative samples are an indispensable inevitability for true research, thus, unfair data’s will never show the real result and all in turn shows inaccurate data- analysis.

Methodological errors – There can be a list of methodological errors that can lead to off beam data-analysis –

Application of wrong methods while calculating data’s is the first and the most common slip-up when it comes to numeric computation.

Lack of concentration can lead to wrong calculation and thus wrong result.

Lack of knowledge and unawareness about different variables and factors to be included during statistical calculations also lead to wide of the mark data analysis.

Interpretation error – Apart from source and methodological mistakes, interpretation errors are another point to consider. Gap between differences of assumed result and calculated statistical upshots are those that may lead to interpretation errors. Some scholars may set up their own assumed result, whereas others may blindly accept the calculated result without cross checking it. These sometime results in interpretation error. Measurement errors also may bias the covariance of two quantities towards the covariance in their error distribution. This variance diverges from the actual result and covariance may appear weaker or stronger at the end.